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#' Compare SCESet objects
#'
#' Combine the data from several SCESet objects and produce some basic plots
#'
#' @param sces named list of SCESet objects to combine and compare.
#' @param point.size size of points in scatter plots.
#' @param point.alpha opacity of points in scatter plots.
#' @param fits whether to include fits in scatter plots.
#' @param colours vector of colours to use for each dataset.
#' \item{\code{FeatureData}}{Combined feature data from the provided
#' SCESets.}
#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
#' \item{\code{Plots}}{Comparison plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean distribution.}
#' \item{\code{Variances}}{Boxplot of variance distribution.}
#' \item{\code{MeanVar}}{Scatter plot with fitted lines showing the
#' mean-variance relationship.}
#' \item{\code{LibraySizes}}{Boxplot of the library size
#' distribution.}
#' \item{\code{ZerosGene}}{Boxplot of the percentage of each gene
#' that is zero.}
#' \item{\code{ZerosCell}}{Boxplot of the percentage of each cell
#' that is zero.}
#' \item{\code{MeanZeros}}{Scatter plot with fitted lines showing
#' the mean-dropout relationship.}
#' }
#' }
#' }
#'
#' The plots returned by this function are created using
#' \code{\link[ggplot2]{ggplot}} and are only a sample of the kind of plots you
#' might like to consider. The data used to create these plots is also returned
#' and should be in the correct format to allow you to create further plots
#' using \code{\link[ggplot2]{ggplot}}.
#'
#' @return List containing the combined datasets and plots.
#' @examples
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
#' names(comparison)
#' names(comparison$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth geom_boxplot
#' scale_y_continuous scale_y_log10 scale_x_log10 xlab ylab ggtitle
compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
fits = TRUE, colours = NULL) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 1, names = "unique")
checkmate::assertNumber(point.size, finite = TRUE)
checkmate::assertNumber(point.alpha, lower = 0, upper = 1)
checkmate::assertLogical(fits, any.missing = FALSE, len = 1)
if (!is.null(colours)) {
checkmate::assertCharacter(colours, any.missing = FALSE,
len = length(sces))
} else {
colours <- scales::hue_pal()(length(sces))
}
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$Dataset <- name
pData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
cpm(sce) <- edgeR::cpm(counts(sce))
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
sces[[name]] <- sce
}
fData.all <- fData(sces[[1]])
pData.all <- pData(sces[[1]])
if (length(sces) > 1) {
for (name in names(sces)[-1]) {
fData.all <- rbindMatched(fData.all, fData(sce))
pData.all <- rbindMatched(pData.all, pData(sce))
}
}
fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
aes_string(x = "Dataset", y = "MeanLogCPM",
#geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_boxplot() +
ylab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
ggtitle("Distribution of mean expression") +
#geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_boxplot() +
ylab("CPM Variance") +
ggtitle("Distribution of variance") +
theme_minimal()
mean.var <- ggplot(fData.all,
aes_string(x = "MeanLogCPM", y = "VarLogCPM",
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Variance ", log[2], "(CPM + 1)"))) +
ggtitle("Mean-variance relationship") +
theme_minimal()
libs <- ggplot(pData.all,
aes_string(x = "Dataset", y = "total_counts",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(labels = scales::comma) +
ylab("Total counts per cell") +
ggtitle("Distribution of library sizes") +
theme_minimal()
z.gene <- ggplot(fData.all,
aes_string(x = "Dataset", y = "pct_dropout",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
ylab("Percentage zeros per gene") +
ggtitle("Distribution of zeros per gene") +
theme_minimal()
z.cell <- ggplot(pData.all,
aes_string(x = "Dataset", y = "pct_dropout",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
ylab("Percentage zeros per cell") +
ggtitle("Distribution of zeros per cell") +
theme_minimal()
aes_string(x = "MeanCounts", y = "pct_dropout",
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
ggtitle("Mean-dropout relationship") +
theme_minimal()
if (fits) {
mean.var <- mean.var + geom_smooth()
mean.zeros <- mean.zeros + geom_smooth()
}
comparison <- list(FeatureData = fData.all,
PhenoData = pData.all,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
LibrarySizes = libs,
ZerosGene = z.gene,
#' Diff SCESet objects
#'
#' Combine the data from several SCESet objects and produce some basic plots
#' comparing them to a reference.
#'
#' @param sces named list of SCESet objects to combine and compare.
#' @param ref string giving the name of the SCESet to use as the reference
#' @param point.size size of points in scatter plots.
#' @param point.alpha opacity of points in scatter plots.
#' @param fits whether to include fits in scatter plots.
#' @param colours vector of colours to use for each dataset.
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#'
#' @details
#'
#' This function aims to look at the differences between a reference SCESet and
#' one or more others. It requires each SCESet to have the same dimensions.
#' Properties are compared by ranks, for example when comparing the means the
#' values are ordered and the differences between the reference and another
#' dataset plotted. A series of Q-Q plots are also returned.
#'
#' The returned list has five items:
#'
#' \describe{
#' \item{\code{Reference}}{The SCESet used as the reference.}
#' \item{\code{FeatureData}}{Combined feature data from the provided
#' SCESets.}
#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
#' \item{\code{Plots}}{Difference plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean differences.}
#' \item{\code{Variances}}{Boxplot of variance differences.}
#' \item{\code{MeanVar}}{Scatter plot showing the difference from
#' the reference variance across expression ranks.}
#' \item{\code{LibraySizes}}{Boxplot of the library size
#' differences.}
#' \item{\code{ZerosGene}}{Boxplot of the differences in the
#' percentage of each gene that is zero.}
#' \item{\code{ZerosCell}}{Boxplot of the differences in the
#' percentage of each cell that is zero.}
#' \item{\code{MeanZeros}}{Scatter plot showing the difference from
#' the reference percentage of zeros across expression ranks.}
#' }
#' }
#' \item{\code{QQPlots}}{Quantile-Quantile plots
#' \describe{
#' \item{\code{Means}}{Q-Q plot of the means.}
#' \item{\code{Variances}}{Q-Q plot of the variances.}
#' \item{\code{LibrarySizes}}{Q-Q plot of the library sizes.}
#' \item{\code{ZerosGene}}{Q-Q plot of the percentage of zeros per
#' gene.}
#' \item{\code{ZerosCell}}{Q-Q plot of the percentage of zeros per
#' cell.}
#' }
#' }
#' }
#'
#' The plots returned by this function are created using
#' \code{\link[ggplot2]{ggplot}} and are only a sample of the kind of plots you
#' might like to consider. The data used to create these plots is also returned
#' and should be in the correct format to allow you to create further plots
#' using \code{\link[ggplot2]{ggplot}}.
#'
#' @return List containing the combined datasets and plots.
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' names(difference)
#' names(difference$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_boxplot xlab ylab
diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
fits = TRUE, colours = NULL) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 2, names = "unique")
checkmate::assertString(ref)
checkmate::assertNumber(point.size, finite = TRUE)
checkmate::assertNumber(point.alpha, lower = 0, upper = 1)
checkmate::assertLogical(fits, any.missing = FALSE, len = 1)
if (!(ref %in% names(sces))) {
stop("'ref' must be the name of an SCESet in 'sces'")
}
if (!is.null(colours)) {
checkmate::assertCharacter(colours, any.missing = FALSE,
len = length(sces) - 1)
} else {
colours <- scales::hue_pal()(length(sces))
}
ref.dim <- dim(sces[[ref]])
for (name in names(sces)) {
sce <- sces[[name]]
if (!identical(dim(sce), ref.dim)) {
stop("SCESets must have the same dimensions")
}
fData(sce)$Dataset <- name
pData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
cpm(sce) <- edgeR::cpm(counts(sce))
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
sces[[name]] <- sce
}
ref.sce <- sces[[ref]]
ref.means <- sort(fData(ref.sce)$MeanLogCPM)
ref.vars <- sort(fData(ref.sce)$VarLogCPM)
ref.libs <- sort(pData(ref.sce)$total_counts)
ref.z.gene <- sort(fData(ref.sce)$pct_dropout)
ref.z.cell <- sort(pData(ref.sce)$pct_dropout)
ref.rank.ord <- order(fData(ref.sce)$exprs_rank)
ref.vars.rank <- fData(ref.sce)$VarLogCPM[ref.rank.ord]
ref.z.gene.rank <- fData(ref.sce)$pct_dropout[ref.rank.ord]
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$RefRankMeanLogCPM <- ref.means[rank(fData(sce)$MeanLogCPM)]
fData(sce)$RankDiffMeanLogCPM <- fData(sce)$MeanLogCPM -
fData(sce)$RefRankVarLogCPM <- ref.vars[rank(fData(sce)$VarLogCPM)]
fData(sce)$RankDiffVarLogCPM <- fData(sce)$VarLogCPM -
fData(sce)$RefRankVarLogCPM
pData(sce)$RefRankLibSize <- ref.libs[rank(pData(sce)$total_counts)]
pData(sce)$RankDiffLibSize <- pData(sce)$total_counts -
pData(sce)$RefRankLibSize
fData(sce)$RefRankZeros <- ref.z.gene[rank(fData(sce)$pct_dropout)]
fData(sce)$RankDiffZeros <- fData(sce)$pct_dropout -
fData(sce)$RefRankZeros
pData(sce)$RefRankZeros <- ref.z.cell[rank(pData(sce)$pct_dropout)]
pData(sce)$RankDiffZeros <- pData(sce)$pct_dropout -
pData(sce)$RefRankZeros
fData(sce)$MeanRankVarDiff <- fData(sce)$VarLogCPM -
ref.vars.rank[fData(sce)$exprs_rank]
fData(sce)$MeanRankZerosDiff <- fData(sce)$pct_dropout -
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sces[[name]] <- sce
}
ref.sce <- sces[[ref]]
sces[[ref]] <- NULL
fData.all <- fData(sces[[1]])
pData.all <- pData(sces[[1]])
if (length(sces) > 1) {
for (name in names(sces)[-1]) {
sce <- sces[[name]]
fData.all <- rbindMatched(fData.all, fData(sce))
pData.all <- rbindMatched(pData.all, pData(sce))
}
}
fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
means <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffMeanLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
ylab(expression(paste("Rank difference mean ", log[2], "(CPM + 1)"))) +
ggtitle("Difference in mean expression") +
theme_minimal()
vars <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffVarLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
ylab(expression(paste("Rank difference variance ", log[2],
"(CPM + 1)"))) +
ggtitle("Difference in variance") +
theme_minimal()
mean.var <- ggplot(fData.all,
aes_string(x = "exprs_rank", y = "MeanRankVarDiff",
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab("Expression rank") +
ylab(expression(paste("Difference in variance ", log[2],
"(CPM + 1)"))) +
ggtitle("Difference in mean-variance relationship") +
theme_minimal()
libs <- ggplot(pData.all,
aes_string(x = "Dataset", y = "RankDiffLibSize",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
ylab(paste("Rank difference libray size")) +
ggtitle("Difference in library sizes") +
theme_minimal()
z.gene <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
ylab(paste("Rank difference percentage zeros")) +
ggtitle("Difference in zeros per gene") +
theme_minimal()
z.cell <- ggplot(pData.all,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
ylab(paste("Rank difference percentage zeros")) +
ggtitle("Difference in zeros per cell") +
theme_minimal()
mean.zeros <- ggplot(fData.all,
aes_string(x = "exprs_rank", y = "MeanRankZerosDiff",
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab("Expression rank") +
ylab("Difference in percentage zeros per gene") +
ggtitle("Difference in mean-zeros relationship") +
theme_minimal()
means.qq <- ggplot(fData.all,
aes_string(x = "RefRankMeanLogCPM", y = "MeanLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab(expression(paste("Reference mean ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Alternative mean ", log[2], "(CPM + 1)"))) +
ggtitle("Ranked means") +
theme_minimal()
vars.qq <- ggplot(fData.all,
aes_string(x = "RefRankVarLogCPM", y = "VarLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab(expression(paste("Reference variance ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Alternative variance ", log[2], "(CPM + 1)"))) +
ggtitle("Ranked variances") +
theme_minimal()
libs.qq <- ggplot(pData.all,
aes_string(x = "RefRankLibSize", y = "total_counts",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference library size") +
ylab("Alternative library size") +
ggtitle("Ranked library sizes") +
theme_minimal()
z.gene.qq <- ggplot(fData.all,
aes_string(x = "RefRankZeros", y = "pct_dropout",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference percentage zeros") +
ylab("Alternative percentage zeros") +
ggtitle("Ranked percentage zeros per gene") +
theme_minimal()
z.cell.qq <- ggplot(pData.all,
aes_string(x = "RefRankZeros", y = "pct_dropout",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference percentage zeros") +
ylab("Alternative percentage zeros") +
ggtitle("Ranked percentage zeros per cell") +
theme_minimal()
if (fits) {
mean.var <- mean.var + geom_smooth()
mean.zeros <- mean.zeros + geom_smooth()
}
comparison <- list(Reference = ref.sce,
FeatureData = fData.all,
PhenoData = pData.all,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
LibrarySizes = libs,
ZerosGene = z.gene,
ZerosCell = z.cell,
MeanZeros = mean.zeros),
QQPlots = list(Means = means.qq,
Variances = vars.qq,
LibrarySizes = libs.qq,
ZerosGene = z.gene.qq,
ZerosCell = z.cell.qq))
return(comparison)
}
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#' Make comparison panel
#'
#' Combine the plots from \code{combineSCESets} into a single panel.
#'
#' @param comp list returned by \code{\link{combineSCESets}}.
#'
#' @return Combined panel plot
#'
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
#' panel <- makeCompPanel(comparison)
#'
#' @importFrom ggplot2 theme
#' @export
makeCompPanel <- function(comp,
labels = c("A", "B", "C", "D", "E", "F", "G", "")) {
if (!requireNamespace("cowplot", quietly = TRUE)) {
stop("The `cowplot` package is required to make panels.")
}
p1 <- comp$Plots$Means + theme(legend.position = "none",
axis.title.x = element_blank())
p2 <- comp$Plots$Variances + theme(legend.position = "none",
axis.title.x = element_blank())
p3 <- comp$Plots$MeanVar + theme(legend.position = "none")
p4 <- comp$Plots$LibrarySizes + theme(legend.position = "none",
axis.title.x = element_blank())
p5 <- comp$Plots$ZerosGene + theme(legend.position = "none",
axis.title.x = element_blank())
p6 <- comp$Plots$ZerosCell + theme(legend.position = "none",
axis.title.x = element_blank())
p7 <- comp$Plots$MeanZeros + theme(legend.position = "none")
leg <- cowplot::get_legend(p1 + theme(legend.position = "bottom"))
#panel <- cowplot::plot_grid(p1, p2, p3, p4, p5, p6, p7, leg,
# ncol = 2,
# labels = c("A", "B", "C", "D", "E", "F", "G", ""))
panel <- cowplot::ggdraw() +
cowplot::draw_label(labels[1], 0.01, 0.97, fontface = "bold") +
cowplot::draw_plot(p1, 0.0, 0.75, 0.5, 0.20) +
cowplot::draw_label(labels[2], 0.51, 0.97, fontface = "bold") +
cowplot::draw_plot(p2, 0.5, 0.75, 0.5, 0.20) +
cowplot::draw_label(labels[3], 0.01, 0.72, fontface = "bold") +
cowplot::draw_plot(p3, 0.0, 0.50, 0.5, 0.20) +
cowplot::draw_label(labels[4], 0.51, 0.72, fontface = "bold") +
cowplot::draw_plot(p4, 0.5, 0.50, 0.5, 0.20) +
cowplot::draw_label(labels[5], 0.01, 0.47, fontface = "bold") +
cowplot::draw_plot(p5, 0.0, 0.25, 0.5, 0.20) +
cowplot::draw_label(labels[6], 0.51, 0.47, fontface = "bold") +
cowplot::draw_plot(p6, 0.5, 0.25, 0.5, 0.20) +
cowplot::draw_label(labels[7], 0.01, 0.22, fontface = "bold") +
cowplot::draw_plot(p7, 0.0, 0.00, 0.5, 0.20) +
cowplot::draw_label(labels[8], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(leg, 0.5, 0.00, 0.5, 0.20)
return(panel)
}
#' Make difference panel
#'
#' Combine the plots from \code{diffSCESets} into a single panel.
#'
#' @param comp list returned by \code{\link{diffSCESets}}.
#'
#' @return Combined panel plot
#'
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' panel <- makeCompPanel(difference)
#'
#' @importFrom ggplot2 theme
#' @export
makeDiffPanel <- function(diff,
labels = c("A", "B", "C", "D", "E", "F", "G", "H",
"I", "J", "K", "L", "")) {
if (!requireNamespace("cowplot", quietly = TRUE)) {
stop("The `cowplot` package is required to make panels.")
}
p1 <- diff$Plots$Means + theme(legend.position = "none",
axis.title.x = element_blank())
p2 <- diff$QQPlots$Means + theme(legend.position = "none")
p3 <- diff$Plots$Variances + theme(legend.position = "none",
axis.title.x = element_blank())
p4 <- diff$QQPlots$Variances + theme(legend.position = "none")
p5 <- diff$Plots$MeanVar + theme(legend.position = "none")
p6 <- diff$Plots$LibrarySizes + theme(legend.position = "none",
axis.title.x = element_blank())
p7 <- diff$QQPlots$LibrarySizes + theme(legend.position = "none")
p8 <- diff$Plots$ZerosCell + theme(legend.position = "none",
axis.title.x = element_blank())
p9 <- diff$QQPlots$ZerosCell + theme(legend.position = "none")
p10 <- diff$Plots$ZerosGene + theme(legend.position = "none",
axis.title.x = element_blank())
p11 <- diff$QQPlots$ZerosGene + theme(legend.position = "none")
p12 <- diff$Plots$MeanZeros + theme(legend.position = "none")
leg <- cowplot::get_legend(p1 + theme(legend.position = "bottom"))
# panel <- cowplot::ggdraw() +
# #cowplot::draw_label(labels[1], 0.01, 0.97, fontface = "bold") +
# cowplot::draw_plot(p1, 0.0, 0.86, 0.5, 0.14) +
# #cowplot::draw_label(labels[2], 0.51, 0.97, fontface = "bold") +
# cowplot::draw_plot(p2, 0.5, 0.86, 0.5, 0.14) +
# #cowplot::draw_label(labels[3], 0.01, 0.72, fontface = "bold") +
# cowplot::draw_plot(p3, 0.0, 0.72, 0.5, 0.14) +
# #cowplot::draw_label(labels[4], 0.51, 0.72, fontface = "bold") +
# cowplot::draw_plot(p4, 0.5, 0.72, 0.5, 0.14) +
# #cowplot::draw_label(labels[5], 0.01, 0.47, fontface = "bold") +
# cowplot::draw_plot(p5, 0.0, 0.58, 1.0, 0.14) +
# #cowplot::draw_label(labels[6], 0.51, 0.47, fontface = "bold") +
# cowplot::draw_plot(p6, 0.0, 0.44, 0.5, 0.14) +
# #cowplot::draw_label(labels[7], 0.01, 0.22, fontface = "bold") +
# cowplot::draw_plot(p7, 0.5, 0.44, 0.5, 0.14) +
# #cowplot::draw_label(labels[8], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p8, 0.0, 0.30, 0.5, 0.14) +
# #cowplot::draw_label(labels[9], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p9, 0.5, 0.30, 0.5, 0.14) +
# #cowplot::draw_label(labels[10], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p10, 0.0, 0.16, 0.5, 0.14) +
# #cowplot::draw_label(labels[11], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p11, 0.5, 0.16, 0.5, 0.14) +
# #cowplot::draw_label(labels[12], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p12, 0.0, 0.02, 1.0, 0.14) +
# #cowplot::draw_label(labels[13], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(leg, 0.0, 0.00, 1.0, 0.02)
# panel <- cowplot::ggdraw() +
# #cowplot::draw_label(labels[1], 0.01, 0.97, fontface = "bold") +
# cowplot::draw_plot(p1, 0.0, 0.8, 0.3, 0.20) +
# #cowplot::draw_label(labels[2], 0.51, 0.97, fontface = "bold") +
# cowplot::draw_plot(p2, 0.3, 0.8, 0.3, 0.20) +
# #cowplot::draw_label(labels[3], 0.01, 0.72, fontface = "bold") +
# cowplot::draw_plot(p3, 0.0, 0.6, 0.3, 0.20) +
# #cowplot::draw_label(labels[4], 0.51, 0.72, fontface = "bold") +
# cowplot::draw_plot(p4, 0.3, 0.6, 0.3, 0.20) +
# #cowplot::draw_label(labels[5], 0.01, 0.47, fontface = "bold") +
# cowplot::draw_plot(p5, 0.6, 0.6, 0.4, 0.40) +
# #cowplot::draw_label(labels[6], 0.51, 0.47, fontface = "bold") +
# cowplot::draw_plot(p6, 0.0, 0.4, 0.3, 0.20) +
# #cowplot::draw_label(labels[7], 0.01, 0.22, fontface = "bold") +
# cowplot::draw_plot(p7, 0.3, 0.4, 0.3, 0.20) +
# #cowplot::draw_label(labels[8], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p8, 0.0, 0.2, 0.3, 0.20) +
# #cowplot::draw_label(labels[9], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p9, 0.3, 0.2, 0.3, 0.20) +
# #cowplot::draw_label(labels[10], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p12, 0.6, 0.2, 0.4, 0.40) +
# #cowplot::draw_label(labels[11], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p10, 0.0, 0.0, 0.3, 0.20) +
# #cowplot::draw_label(labels[12], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(p11, 0.3, 0.0, 0.3, 0.20) +
# #cowplot::draw_label(labels[13], 0.51, 0.22, fontface = "bold") +
# cowplot::draw_plot(leg, 0.6, 0.0, 0.4, 0.20)
panel <- cowplot::ggdraw() +
#cowplot::draw_label(labels[1], 0.01, 0.97, fontface = "bold") +
cowplot::draw_plot(p1, 0.0, 0.6, 0.2, 0.4) +
#cowplot::draw_label(labels[2], 0.51, 0.97, fontface = "bold") +
cowplot::draw_plot(p2, 0.0, 0.2, 0.2, 0.4) +
#cowplot::draw_label(labels[3], 0.01, 0.72, fontface = "bold") +
cowplot::draw_plot(p3, 0.2, 0.6, 0.2, 0.4) +
#cowplot::draw_label(labels[4], 0.51, 0.72, fontface = "bold") +
cowplot::draw_plot(p4, 0.2, 0.2, 0.2, 0.4) +
#cowplot::draw_label(labels[5], 0.01, 0.47, fontface = "bold") +
cowplot::draw_plot(p5, 0.0, 0.0, 0.4, 0.2) +
#cowplot::draw_label(labels[6], 0.51, 0.47, fontface = "bold") +
cowplot::draw_plot(p6, 0.4, 0.6, 0.2, 0.4) +
#cowplot::draw_label(labels[7], 0.01, 0.22, fontface = "bold") +
cowplot::draw_plot(p7, 0.4, 0.2, 0.2, 0.4) +
#cowplot::draw_label(labels[8], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(p8, 0.6, 0.6, 0.2, 0.4) +
#cowplot::draw_label(labels[9], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(p9, 0.6, 0.2, 0.2, 0.4) +
#cowplot::draw_label(labels[10], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(p12, 0.4, 0.0, 0.4, 0.2) +
#cowplot::draw_label(labels[11], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(p10, 0.8, 0.6, 0.2, 0.4) +
#cowplot::draw_label(labels[12], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(p11, 0.8, 0.2, 0.2, 0.4) +
#cowplot::draw_label(labels[13], 0.51, 0.22, fontface = "bold") +
cowplot::draw_plot(leg, 0.8, 0.0, 0.2, 0.2)
return(panel)
}